Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA.
Pharmacoepidemiol Drug Saf. 2011 Aug;20(8):849-57. doi: 10.1002/pds.2152. Epub 2011 Jun 30.
A semi-automated high-dimensional propensity score (hd-PS) algorithm has been proposed to adjust for confounding in claims databases. The feasibility of using this algorithm in other types of healthcare databases is unknown.
We estimated the comparative safety of traditional non-steroidal anti-inflammatory drugs (NSAIDs) and selective COX-2 inhibitors regarding the risk of upper gastrointestinal bleeding (UGIB) in The Health Improvement Network, an electronic medical record (EMR) database in the UK. We compared the adjusted effect estimates when the confounders were identified using expert knowledge or the semi-automated hd-PS algorithm.
Compared with the 411,616 traditional NSAID initiators, the crude odds ratio (OR) of UGIB was 1.50 (95%CI: 0.98, 2.28) for the 43,569 selective COX-2 inhibitor initiators. The OR dropped to 0.81 (0.52, 1.27) upon adjustment for known risk factors for UGIB that are typically available in both claims and EMR databases. The OR remained similar when further adjusting for covariates--smoking, alcohol consumption, and body mass index-that are not typically recorded in claims databases (OR 0.81; 0.51, 1.26) or adding 500 empirically identified covariates using the hd-PS algorithm (OR 0.78; 0.49, 1.22). Adjusting for age and sex plus 500 empirically identified covariates produced an OR of 0.87 (0.56, 1.34).
The hd-PS algorithm can be implemented in pharmacoepidemiologic studies that use primary care EMR databases such as The Health Improvement Network. For the NSAID-UGIB association for which major confounders are well known, further adjustment for covariates selected by the algorithm had little impact on the effect estimate.
已经提出了一种半自动高维倾向评分（hd-PS）算法，以调整索赔数据库中的混杂因素。该算法在其他类型的医疗保健数据库中的可行性尚不清楚。
我们使用英国电子病历（EMR）数据库 Health Improvement Network 来评估传统非甾体抗炎药（NSAIDs）和选择性 COX-2 抑制剂在发生上消化道出血（UGIB）方面的相对安全性。我们比较了当使用专家知识或半自动 hd-PS 算法识别混杂因素时调整后的效果估计值。
与 411616 名传统 NSAID 启动者相比，43569 名选择性 COX-2 抑制剂启动者的 UGIB 粗比值比（OR）为 1.50（95%CI：0.98，2.28）。当调整 UGIB 的已知危险因素（通常在索赔和 EMR 数据库中都可用）时，OR 下降至 0.81（0.52，1.27）。当进一步调整通常不在索赔数据库中记录的协变量（吸烟、饮酒和体重指数）或使用 hd-PS 算法添加 500 个经验确定的协变量时，OR 保持相似（OR 0.81；0.51，1.26）或添加 500 个经验确定的协变量使用 hd-PS 算法（OR 0.78；0.49，1.22）。当调整年龄和性别以及 500 个经验确定的协变量时，OR 为 0.87（0.56，1.34）。
hd-PS 算法可用于使用初级保健 EMR 数据库（如 Health Improvement Network）进行药物流行病学研究。对于 NSAID-UGIB 关联，已知主要混杂因素，算法选择的协变量进一步调整对效果估计值影响不大。
